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Related Experiment Video

Updated: Feb 12, 2026

Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry
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Evaluation of batch effect elimination using quality control replicates in LC-MS metabolite profiling.

Ángel Sánchez-Illana1, Jose David Piñeiro-Ramos1, Juan Daniel Sanjuan-Herráez2

  • 1Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.

Analytica Chimica Acta
|April 8, 2018
PubMed
Summary

Batch effects in untargeted LC-MS metabolomics hinder data reliability. This study evaluates multiple assessment tools, finding that combining qualitative and quantitative methods, especially using quality control samples, effectively detects and manages these variations.

Keywords:
Batch effectLC-MSMetabolomicsQC-RSCQC-SVRC

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics often suffers from systematic instrument response variations within and between batches.
  • These batch effects reduce statistical power, negatively impacting the repeatability and reproducibility of metabolomic studies.
  • Standardized methods for assessing and correcting LC-MS batch effects are lacking, making optimal approach selection challenging.

Purpose of the Study:

  • To explore the effectiveness of various qualitative and quantitative tools for assessing batch effects in untargeted LC-MS metabolomics.
  • To determine if limiting batch effect assessment to quality control (QC) samples enhances detection power.
  • To propose a method for comparing and tailoring batch effect elimination strategies.

Main Methods:

  • Qualitative assessment included monitoring spiked internal standards, principal component analysis (PCA), and hierarchical cluster analysis (HCA).
  • Quantitative assessment utilized distributions of relative standard deviation in QCs (RSDQC), median Pearson correlation coefficients in QCs, runs tests for random features in QCs, and multivariate tools (δ-statistic, Silhouette plots, Principal Variance Component Analysis, expected technical variation).
  • The study focused on evaluating these tools using QC samples to assess both within- and between-batch effects.

Main Results:

  • Both qualitative and quantitative assessment approaches were found to be complementary in evaluating batch effects.
  • Limiting the analysis to QC samples significantly increased the power to detect and evaluate within- and between-batch effects.
  • Graphical integration of outputs from multiple quantitative tools facilitated batch effect evaluation.

Conclusions:

  • A combination of qualitative and quantitative methods, with a focus on QC samples, provides a powerful approach for assessing batch effects in untargeted LC-MS metabolomics.
  • The graphical integration of multiple quantitative tool outputs offers a straightforward strategy for comparing and tailoring batch effect correction methods.
  • This integrated approach enhances the reliability and reproducibility of metabolomic data analysis.